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Miguel Delgado-Rodríguez and Javier Llorca’s article on bias in
health services and medical research is instructive and cautionary [1].
The extensive glossary of biases is thought provoking and might
beneficially be introduced as required reading for all researchers.
The glossary might be usefully updated by the addition of a form of
selection bias which is very much “of our time”, having be...
Miguel Delgado-Rodríguez and Javier Llorca’s article on bias in
health services and medical research is instructive and cautionary [1].
The extensive glossary of biases is thought provoking and might
beneficially be introduced as required reading for all researchers.
The glossary might be usefully updated by the addition of a form of
selection bias which is very much “of our time”, having been born of
relatively recent developments in data protection law and medical ethics
which have seen the confidentiality and privacy of patients more
rigorously protected than ever before [2, 3]. As a result of these
developments, there exists an almost universal requirement for prior
written consent before researchers can access data contained in patients’
medical records: immensely rich sources of data relating to morbidity and
process of care, sometimes spanning lifetimes. Observational research has
in the past been able to study illness and healthcare in large,
representative – even whole – populations in real life community and
routine clinical settings and in contexts where recruitment to trials has
not been possible, such as immediately following stroke. The requirement
for prior written consent represents a threat to such observational
research and epidemiology: work is put at risk which could be of practical
benefit to society in order to guard against limited and largely
theoretical harms to individuals.
“Consent bias” is a term which has been coined to describe the
selection bias that may result from the loss of non-consenters to cohorts
in observational research [4]. It is a bias not unlike the “healthy
volunteer effect” described by Delgado-Rodríguez and Llorca, except that
consent bias occurs at the outset of a study and the trend may be towards
inclusion of disproportionate numbers of healthy or unhealthy volunteers.
Demonstrating the effect empirically is only possible in relatively
rare research circumstances where data can be compared for non-consenters
and consenters. Where this has been possible the evidence has suggested
that men are more likely than women to consent to the use of their
personal medical records by researchers and that age and socio-economic
status are also predictors [5-7]. A UK study demonstrated the potential
effect of consent bias in one very specialised disease area: it was shown
in a population affected by intracranial vascular malformation that if
data from non-consenters had been lost to the study, it would have been
unable to identify or confirm the prognostic importance of a risk factor
which in clinical practice often influences the decision to treat [4]. A
recent Irish study considered the implications of consent bias amongst a
large community cohort of people with established ischaemic heart disease,
a disease area of everyday clinical importance in Europe. The study had
original data for all cohort members and also recorded consent
preferences. The implications of the study were that if future cohorts of
people with IHD are dependent upon prior written consent they are likely
to contain disproportionate numbers of men, of those who have given up
smoking, who have previously benefited from health care interventions or
those whose clinical risk factors such as cholesterol and blood pressure
levels are already well managed [8].
The potential consequences for observational research and
epidemiology are grave: the generalisability of such research may be
reduced; the effects of treatments may be overestimated or underestimated
if those who are most unwell or who are not making healthy lifestyle
decisions are under-represented in study populations; and the
identification of as yet unrecognised prognostic risk factors may be put
at risk. This being the case, consent bias deserves a place in any
glossary.
References
1. Delgado-Rodrýguez M, Llorca J. Bias. J Epidemiol Community Health
2004;58:635-641.
2. Personal Information in Medical Research. London: Medical Research
Council; 2000.
3. Personal data for public good: using health information in medical
research. A Report from the Academy of Medical Sciences. London: The
Academy of Medical Sciences; 2006.
4. Al-Shahi R, Vousden C, Warlow C. Bias from requiring explicit consent
from all participants in observational research: prospective, population
based study. BMJ 2005;331:942-7.
5. Angus VC, Entwistle VA, Emslie MJ, Walker KA, Andrew JE. The
requirement for prior consent to participate on survey response rates: a
population-based survey in Grampian. BMC Health Services Research
2003;3:21.
6. Jacobsen SJ, Xia Z, Campion ME, Darby CH, Plevak MF, Seltman KD, et al.
Potential effect of authorization bias on medical record research. Mayo
Clin Proc 1999;74:330-338.
7. Tu JV, Willison DJ, Silver FL, Fang J, Richards JA, Laupacis A, et al.
Impracticability of informed consent in the registry of the Canadian
stroke network. N Engl J Med 2004;350(14):1414-1421.
8. Buckley B, Murphy AW, Byrne M, Glynn LG. Selection bias resulting from
the requirement for prior consent in observational research: a community
cohort of people with ischaemic heart disease. Heart 2007;In press:
accepted for publication, Jan 2007.
In the latest issue of the JECH, Rezeaiean, Dunn, St Leger and
Appleby provide a multidisciplinary glossary on geographical epidemiology,
spatial analysis and geographical information systems. The glossary in
large is useful as it gives an overview of relevant methodological
concepts. However, in the section on disease clustering the Authors
shortly describe geographical machine analysis and spatial sc...
In the latest issue of the JECH, Rezeaiean, Dunn, St Leger and
Appleby provide a multidisciplinary glossary on geographical epidemiology,
spatial analysis and geographical information systems. The glossary in
large is useful as it gives an overview of relevant methodological
concepts. However, in the section on disease clustering the Authors
shortly describe geographical machine analysis and spatial scan and faulty
conclude that “The spatial scan statistic has an advantage over
geographical analysis machine in taking into account the problem of
multiple testing.”. The description of the two methods is not correct and
the conclusion is not justified.
Wakefield, Kelsall and Morris, in the book on “Spatial epidemiology”
(Elliott et al, Eds, 2000) mention the spatial scan and geographical
machine methods in a chapter on clustering, cluster detection and spatial
variation in risk. The “moving window” methods included in this chapter,
although relevant, are not appropriate when studying infectious diseases.
Their application is in general valuable although reduced to exploration
in opposite to explanation. This partly due to the mathematical concept of
clustering in terms of independent cases. Depending on this definition and
the chosen parameters the clusters may appear or disappear. Thus, only
epidemiological motivated study makes sense. The definition of a “cluster”
is crucial since circles may be constructed to have constant radius (e.g.
geographical machine analysis), constant population at risk (e.g. spatial
scan) or constant number of cases. The choice of a significance level for
a circle remains a problem.
According to Wakefield and colleagues, the geographical machine
method uses constant radius or a circle centred on a case within a
predefined cluster of varying size k and the radius is such as the k-th
nearest neighbourhood is included. The scan statistics was originally
developed to “scan” across a time region of interest with statistics being
taken as the maximum number of events to occur within windows of constant
size. The fixed window or circle is being compared to an underlying
intensity that is uniform and less than specified population size. This
feature makes this method in a spatial context unreasonable. The largest
difficulty with this method is that the choice of population is arbitrary.
In practice, this method is not used to indicate a single cluster but to
highlight a number of potential clusters. When this is done the properties
of this procedure become unknown. The circles are not completely
comparable since it is the populations and not expected numbers that are
defining the choice of radii, although it is straightforward to use
expected numbers.
The problems above originate from the fact that a form of a circle
does not correspond to the form of any administrative geographical unit,
which often are demarcated by natural barriers, such as rivers, forests,
lakes or mountains and lack perfect geometry. The circles are imposed on
arbitrary areas. This makes it problematic to use epidemiological measures
requiring exact number of persons in the denominator. There is an apparent
risk of over- or underestimation. When many circles are compared the risk
of both arbitrary exclusion and overlapping is obvious, the latter meaning
multiple comparisons of the same cases and members in populations. This
produces a set of not independent tests.
There is a serious critique of both methods as intrinsically flawed
due to the problem of multiple testing. This problem is reduced by Monte
Carlo technique but the Monte Carlo as a surveillance devise does not
provide an estimate of the risk around a putative source, only indicates
the presence of a cluster. The “moving window” methods are only valuable
as tools generating hypothesis in contrast to explanation of an underlying
causality. Unrestricted use of these methods in publications on health
hazards incurs risk of producing media alarms and defeatist public
reactions not motivated on scientific grounds. Scientists are not supposed
to exorcise.
Grazyna T Adamiak
PhD, Master of Health and Welfare, Master of Arts
References
Elliott P, Wakefield J, Best N, Briggs D (Eds). Spatial Epidemiology.
Methods and Applications. Oxford Medical Publications. Oxford University
Press, 2000.
Wakefield J, Kelsall JE, Morris SE. Clustering, cluster detection,
and spatial variation in risk. In: Elliott P, Wakefield J, Best N, Briggs
D (Eds). Spatial Epidemiology. Methods and Applications. Oxford Medical
Publications. Oxford University Press, 2000.
Editor - In this issue of the journal, Dr Peter John Aspinall has
raised a very important issue regarding the question of whether colour
categories for ethnic groups should be abandoned because of abolishment of
colour categories in the Scotland census.[1]
The Scottish population census team deserves congratulations for
breaking the tradition in abandoning the colour categories used in 1991
and 2001.[2] This bol...
Editor - In this issue of the journal, Dr Peter John Aspinall has
raised a very important issue regarding the question of whether colour
categories for ethnic groups should be abandoned because of abolishment of
colour categories in the Scotland census.[1]
The Scottish population census team deserves congratulations for
breaking the tradition in abandoning the colour categories used in 1991
and 2001.[2] This bold decision to replace Black, Black Scottish or Black
British with "African or Caribbean" with option of North Africa, East
Africa, Southern Africa, West Africa, Central Africa or other; "White"
with "European" and its sub-options; and the recognition of other minority
ethnic groups such as Arabs, Gypsy/Traveller and Jewish represent a giant
leap forward and emphasises the importance of moving beyond the simple
black/white category that was the dominant and limiting approach for most
of the 20th century.[3,4,5] Such classifications have very important
implications. For example, it offers the opportunity to identify
vulnerable groups, which are often concealed, so that appropriate actions
can be taken to address their needs. Other regions and countries will
benefit by following the Scottish example.
The Scottish example has very important implications for both public
health and epidemiological research on ethnicity and health. For one,
census data are normally used for social purposes. If the need for proper
ethnic classification is being recognized in this domain, then it
challenges both researchers and professionals in the field of ethnicity
and health who still use colour category for their ethnic groups to
rethink again and recognise the considerable diversity within these
populations. There is still a need for debate on appropriate terminologies
for classifying ethnic groups in public health and epidemiological
research. The question raised by Dr Aspinall on abandoning colour
categories for ethnic groups is a good starting point. Until more
appropriate conceptualisation and definition of ethnic groups is achieved
nationally and internationally, much public health and epidemiological
research on ethnicity and health will continue to remain controversial and
often misleading.[3]
References
1. Aspinall PJ. Is it time to abandon colour categories for ethnic
groups? J Epidemiol Community Health. 2007;61(2):91
2. General Register Office for Scotland. 2006 census test forms.
Edinburgh: GRO(S), 2005, http://www.gro-scotland.gov.uk/files/2006-census-
test-form.pdf (accessed 26 Jan 2007).
3. Agyemang C, Bhopal R, Bruijnzeels M. Negro, Black, Black African,
African Caribbean, African American or what? Labelling African origin
populations in the health arena in the 21st century. J Epidemiol Community
Health 2005;59(12):1014-8.
4. Bhopal R, Donaldson L. White, European, Western, Caucasian, or
what? Inappropriate labeling in research on race, ethnicity, and health.
Am J Public Health 1998;88(9):1303-7.
5. Agyemang C. Ethnic misclassifications hamper progress in research.
BMJ 2006;332(7553):1335.
Studies of changing inequalities in receipt of procedures like that
carried out with respect to revascularization by Hetemaa et al.[1] need to
be undertaken with an appreciation of the statistical tendency whereby the
rarer an outcome the greater the relative difference in rates of
experiencing it and the smaller the relative difference in rates of
avoiding it.[2-6]
Studies of changing inequalities in receipt of procedures like that
carried out with respect to revascularization by Hetemaa et al.[1] need to
be undertaken with an appreciation of the statistical tendency whereby the
rarer an outcome the greater the relative difference in rates of
experiencing it and the smaller the relative difference in rates of
avoiding it.[2-6]
Most research into inequality in the health arena examines morbidity
and mortality. Typically these are examined in terms of relative
differences in experiencing adverse outcomes. In recent decades most,
though not all, adverse health outcomes have been declining and relative
differences in experiencing them have been increasing. Generally these
increases have been regarded as reflecting meaningful worsening of the
relative situation of disadvantaged groups, but without recognition of the
extent to which such increases may be solely the consequences declining
prevalence of the outcomes or recognition that relative differences in
experiencing the opposite outcome may be declining. Whether the observed
patterns of changing relative differences are more than or less than those
that would be expected to flow solely from declines in the prevalence of
the outcome has gone unexamined, though it is not clear that there are
effective tools to answer such questions.[2,6]
Research into inequalities in the receipt of beneficial procedures,
on the other hand, has generally examined rates of experiencing the
favorable outcome (i.e., receipt, rather than denial, of the procedure).
Because rates of receiving these procedures usually have been increasing,
relative differences in rates of receiving them have been declining,
though relative rates of failing to receive them have been increasing. A
greater increase in rates of receiving the procedure experienced by groups
with lower baseline rates of receiving the procedures (relative to proxy
for need), such as that found in the study by Hetemaa et al., is a
corollary to this pattern, as is a smaller decrease in rates of failing to
receive the procedure. Table 1 in the study provides many illustrations
of the pattern. For example, the overall female rate of receiving
revascularization procedures increased 71 percent more than the male rate
(a 317% increase for women compared with a 186% increase for men), but the
female rate of failing to receive the procedure declined by 22 percent
less than the male rate (a 20% decline for women compare with 26% decline
for men). Correspondingly the relative difference in rates of receiving
the procedure decreased while the relative difference in rates of failing
to receive the procedure increased.
The authors note an expectation of declining inequality based on what
has been observed in other situations where there occurred an increased
supply of coronary revascularization procedures. Yet, the pattern of
declining relative differences in receipt of procedures, not only for
revascularization but for all procedures that are increasing, is generally
to expected to occur solely as a result of the increase in supply, as is
an increase in the relative difference in failing to receive the outcome.
Whether either change reflects a meaningful change in inequalities – i.e.,
one that is not solely a consequence of the increasing availability of the
procedure – requires a closer examination. Again, however, it is not
clear that there exist effective tools for doing so.
The above-described tendency is pertinent not only to comparisons of
changes over time, but to all comparisons of relative differences in
settings with differing overall frequencies of an outcome. The authors
note smaller relative differences in procedures in districts where the
procedures are more common and larger relative differences among persons
over 70 (where procedure are rarer). This pattern is to be expected
simply because of the differing frequencies of the procedures in the
different settings. And one would likely find the reverse pattern if one
examined rates of failing to receive the procedure.
That is not to say that these patterns will be observed with respect
to every comparison of the size of relative differences in varying
temporal, demographic, or geographic settings. For factors other than the
referenced statistical tendency are at work as well. Nevertheless, one
cannot evaluate those factors without appreciation of the purely
statistical aspects of the observed patterns.
In the United States, citing the receipt or non-receipt of
mammography as an example, the National Center for Health Statistics
(NCHS) recently recognized that the size and the patterns of change in
health inequalities may turn on whether one examines the favorable or the
adverse outcome.[7] It recommended that all relative differences between
groups be measured in terms of adverse outcomes. If the recommendation is
followed, in many situations where relative differences were perceived to
be declining – as, for example, in the case of male-female
revascularization rates in Finland – the differences would instead be
perceived to be increasing. But NCHS has yet to acknowledge that relative
differences in rates of experiencing favorable and adverse outcomes tend
to change systematically in opposite directions as the prevalence of each
outcome changes or to suggest a means of identifying changes in inequality
that are not solely the consequence of changes in prevalence.
One might think that the NCHS focus on adverse outcomes would be
especially inappropriate for something like revascularization, since, even
among those hospitalized for cardiac heart disease, revascularization is
not appropriate for everyone. The point, however, is that the value of
health inequality studies lies in identifying changes that are not solely
the result of changes in prevalence. Neither changes in relative
differences in receipt of procedures nor changes in relative differences
in denial of procedures – nor changes in absolute differences (which here
favored men)[2,6] – seem to serve that purpose.
James P. Scanlan
References
1. Hetemaa T, Keskimäki I, Manderbacka, et al. How did the recent
increase in the supply or coronary operations in Finland affect
socioeconomic and gender equity in their use? J Epidemiol Community
Health 2003;57:178-185.
2. Scanlan JP. Can we actually measure health disparities? Chance
2006:19(2):47-51:
http://www.jpscanlan.com/images/Can_We_Actually_Measure_Health_Disparities.pdf.
3. Scanlan JP. Measuring health disparities. J Public Health Manag
Pract 2006;12(3):294 [Lttr]:
http://www.nursingcenter.com/library/JournalArticle.asp?Article_ID=641470.
4. Scanlan JP. Race and Mortality. Society. 2000;37(2):19-35:
http://www.jpscanlan.com/images/Race_and_Mortality.pdf.
6. Scanlan JP. The misinterpretation of health inequalities in the
United Kingdom. Paper presented at: British Society for Population Studies
Annual Conference 2006, Southampton, England, Sept. 18-20, 2006:
http://www.jpscanlan.com/images/BSPS_2006_Complete_Paper.pdf.
7. Keppel K., Pamuk E., Lynch J., et al. Methodological issues in
measuring health disparities. Vital Health Stat 2005;2 (141):
http://www.cdc.gov/nchs/data/series/sr_02/sr02_141.pdf.
In their prospective study, Baibas et al (JECH 2005;59(4):274-
8)showed that, in a Greek mountain village at 950 metres, total mortality
and not merely coronary mortality was lower than in two lowland villages.
What follows assumes that the cancer figures included within "other
causes" follow this pattern.
In 'Geographic Cancer Risk and Intracellular Potassium/Sodium
ratios'.Cancer Detection...
In their prospective study, Baibas et al (JECH 2005;59(4):274-
8)showed that, in a Greek mountain village at 950 metres, total mortality
and not merely coronary mortality was lower than in two lowland villages.
What follows assumes that the cancer figures included within "other
causes" follow this pattern.
In 'Geographic Cancer Risk and Intracellular Potassium/Sodium
ratios'.Cancer Detection and Prevention 1986;9:171-94, B. Jansson reported
a high intracellular K+/Na+ ratio correlating with low cancer mortality.
Apart from diet, the favourable ratio of ions is promoted, inter alia at
higher altitudes by an effect involving the sodium pump. Among ten studies
supportive of altitude-low cancer effects cited by Jansson, several show
an altitude effect mainly at sites exposed to air, viz. mouth,
esophagus,larynx and lung. Even 250 metres can make a difference. Jansson
had ideas about the mechanism possibly involved in this cancer
prophylaxis.
Recent animal studies, if applicable to humans, permit further
speculations about a mechanism, viz. relative inhibition of Na+K+ATPase
(causing low intracellular K+/Na+) at low altitudes, leading to cell
detachment from one another and from substrate (Contreras et al.Journal
of Cell Science 1999;112:4223-32).Such detached cells would normally
die from the form of apoptosis named anoikis, but if the cells express the
activated neurotrophic receptor trkB, via a pathway through PI(3)K and
Akt, non-malignant cells can be converted into highly tumorigenic cells
(Douma et al.Nature 2004;430:1034-9).One assumes other factors must be
present.
To isolate the contribution to longevity of physical exertion under
hypoxia, as proposed by Baibas et al, I suggest studying mortality in
towns built on a high plateau, compared with those at similar mean
elevations, which are spread up and down a mountainside.
We read with interest the recent
editorial setting an agenda for future research considering neighbourhood
influences on health.[1] Whilst agreeing with many of the points raised in the editorial we take issue with the proposed use
of multiple membership multilevel models to explore contextual effects at
different points in time.
A recent paper used multiple
membership models to analyse the effect of area of residence observed over 9
years on individuals’ health (measured just at the final time point).[2] An accompanying editorial pointed out the problems with
the use of such models.[3] Since we agree that understanding the longitudinal
influence of area of residence over time will form a key part of the
neighbourhood research agenda we expand on the reasons that make multiple
membership models unsuitable for important research questions pertinent to the
field and propose an alternative model.
If person P1 lived in area A1
at time T1 and in area A2 at time T2 then from a life course
perspective we might expect there to be a contribution of both areas to that
person’s measured or reported health at time T2. Similarly, if person P2 moved in the opposite direction then we would expect
a contribution from both areas. The multiple membership model,
however, makes two simplifying and unrealistic assumptions. Firstly, the effect
of each area is assumed to be the same at both times, having the same effect on
person P1 at time T1 as on person P2 at time T2. This means assuming that
the area effect is not dependent on the period; the regeneration or decay of
areas is ignored. Secondly, the multiple membership model
requires explicit weights to be attached to the contribution of each area to an
individual’s health. Suggesting that this should be done on the basis of the
time spent in each area[1] is to assume that the effect of an area is constant
irrespective of the stage of the life course – in other words, such an analysis
assumes that the risk associated with neighbourhoods accumulates at a steady
rate throughout the life course. So if persons P1 and P2
spent an equal amount of time in each area, the direction of movement (from A1 to A2 or from A2
to A1) is ignored.
Alternative models – such as a critical period model – cannot be investigated
under such a framework.[4] Yet the influence of the social environment along the
life course has been shown to differ for specific diseases.[5]
The authors propose extending the
above model to one where health, as well of area of residence, is measured at
both time points.[1] In addition to the assumptions noted above, the
suggestion that the area of residence at time T2 affects health measured at time T1 clearly departs from the usual model of causation.
A more suitable model for such data
is the cross-classified multilevel model. Figure 1 illustrates the
classification diagram for individuals (at level 1) whose health is measured at
time T2 and who live in a
cross-classification of areas at times T1
and T2. Every individual
lives in an area at each time point, whether these are different areas (P1 and P2) or the same area (P3 and P4).
When health is measured at T1
individuals are nested within areas in a strict hierarchy; when health is
measured at T3 there is a
three-way cross-classification of areas at T1,
T2 and T3. The cross-classified
model does not make the assumption that area effects will be the same at the
two time points; in fact, the assumption that the effects for one area at
different times are uncorrelated is likely to lead to conservative estimates of
the variance components. However, the free estimation of the variances
associated with each time point means that a variety of life course models can
be examined without the need for prior assumptions.
Level 1:
Person
Level 2(1):
Neighbourhood at time T1
Level 2(2):
Neighbourhood at time T2
Figure 1 Multilevel structure
of individuals nested within a cross-classification of areas at two time points
(T1 and T2). Diagram shows person P1 moving from area A1
to area A2 and person P2 moving from area A2 to area A1.
Alastair H Leyland
Senior Research Scientist
MRC Social and Public Health
Sciences Unit, Glasgow, Scotland
Øyvind Næss
Senior Scientist
Epidemiological Division, National
Public Health Institute, Oslo, Norway
Correspondence to:
Alastair H Leyland
MRC Social and Public Health
Sciences Unit
4 Lilybank Gardens
Glasgow G12 8RZ
Scotland
References
1.Kawachi I, Subramanian SV. Neighbourhood influences on
health. Journal of Epidemiology and
Community Health 2007; 61:3-4.
2.Chandola
T, Clarke P, Wiggins RD, et al. Who you live with and where you live: setting
the context for health using multiple membership multilevel models. Journal of Epidemiology and Community Health
2005; 59:170-5.
3.Leyland AH. Assessing the impact of mobility on health: implications for lifecourse epidemiology.Journal of Epidemiology and Community Health 2005; 59:90-1.
4.Kuh
D, Ben-Schlomo Y, Lynch J, et al. Life course
epidemiology. Journal of Epidemiology and
Community Health 2003; 57:778-83.
5.Næss
Ø, Strand BH, Davey Smith G. Childhood and adulthood
socioeconomic position across 20 causes of death. A
prospective cohort study of 800 000 Norwegian men and women.Journal of Epidemiology and
Community Health (in press).
I wish to thank Dr Hanna and her
colleagues for this excellent study (1). I would like to share below a few
comments.We can read, under the “Social
Acceptability” heading:
“Within
the Bangladeshi community smoking was not acceptable as Islam forbids addiction
to any substance. However, it was agreed that smoking was a habit for some
Muslims, although much less acceptable in women than in men. Smoking using a
hookah was uncommon in Scotland owing to the absence of strong sunlight for
drying the tobacco. It was more acceptable to chew paan, which was common
among women and men. It was thought that truthful answers to questions on
smoking might be more likely if the questions were put by a doctor or by an
independent researcher.”
First
off, Islam does not “forbid” many things. It is an extremely tolerant religion;
so tolerant that even Western tobacco control activists are often amazed to see
how anti-tobacco campaigns are difficult to implement in the corresponding
countries (2):
« Lâ
′ikrâha fî-d-dîn » (Let There Be No Compulsion in Religion)
(Qur’ân: II, 255)
Concerning
hookah (shisha, narghile) smoking, I think the questionnaire could have been
enhanced at this point for two main reasons:
1-because
of the tremendous recent development of hookah smoking in the world, already
called an epidemic by some researchers;
2-the
interviewees were probably thinking of the traditional raw tobacco usually
prepared in their remote country. However, more and more people, in the United
Kingdom and other countries of the world, now smoke a hookah with a
ready-to-use tobacco or non-tobacco molasses based mixture called tobamel or
“mu‘assel” (i.e. honeyed in Arabic)(3). In these conditions, their
answer was expected and, I would add, naïve: no sun so no sun-cured tobacco…
This
adapted questionnaire by Hanna and colleagues is, I insist, original and
excellent and I have no doubt that “the methods and lessons are applicable
internationally”. It is not biased as it actually happened with another one
in Lebanon where the interviewees did not know that some questions related to
the supposed established detrimental health effects of hookah smoking were, in
fact, referring to a study based on a “waterpipe” smoking machine in a
laboratory and powered by a type of charcoal (quick self-lighting) thatis not used in their country (4).
(1) Hanna L, Hunt S, Bhopal RS. Cross-cultural adaptation of
a tobacco questionnaire for Punjabi,Cantonese, Urdu and Sylheti speakers:
qualitative research for better clinical practice, cessation services and
research . Journal of Epidemiology and Community Health
2006;60:1034-1039.
(2) Chaouachi K:
Le narguilé : analyse socio-anthropologique. Culture,
convivialité, histoire et tabacologie d’un mode d’usage populaire du tabac.
Doctoral thesis, Université Paris X (France). [Eng.:
Narghile (hookah): a Socio-Anthropological Analysis. Culture,
Conviviality, History and Tobaccologyof a Popular Tobacco Use Mode]. Published by ANRT (Lille), 420
pages.
(3) Chaouachi K. A Critique of the WHO's TobReg "Advisory
Note" entitled: "Waterpipe Tobacco Smoking: Health Effects, Research
Needs and Recommended Actions by Regulators. Journal of Negative Results in
Biomedicine2006 (17 Nov); 5:17.
(4) Chaaya M., Roueiheb
Z.E., Chemaitelly H., Azar G., Nasr J. and Al-Sahab B. Argileh smoking
among university students: A new tobacco epidemic. Nicotine &
Tobacco Research. 2004
Jun; 6 (3):457-63.
The results of the study by Fairley and Leyland [1] of changing
social class inequalities in perinatal outcomes in Scotland must be
interpreted in light of the statistical tendency whereby the rarer an
outcome the greater the relative differences in experiencing the outcome
and the smaller the relative difference in avoiding the outcome.[2-6] In
times of declines in adverse outcome (the more common...
The results of the study by Fairley and Leyland [1] of changing
social class inequalities in perinatal outcomes in Scotland must be
interpreted in light of the statistical tendency whereby the rarer an
outcome the greater the relative differences in experiencing the outcome
and the smaller the relative difference in avoiding the outcome.[2-6] In
times of declines in adverse outcome (the more common situation in recent
decades), the relative difference in experiencing the outcome will tend to
increase solely as a consequence of the decline in prevalence. But such
increases in relative difference in experiencing the outcome – which
usually are attended by declines in the relative difference in rates of
avoiding the outcome as well as declines in the absolute difference
between rates of experiencing (or avoiding) – ought not to be regarded as
a meaningful worsening of the relative situation of disadvantaged groups.
A departure from the pattern in the 1980s, however, might suggest an
improvement in that situation. [2,6]
The same tendency influences the differing patterns observed among
groups categorized by characteristics that are related to overall risk,
such as are shown in Tables 3-5 of the study. The authors note that
inequalities among lone mothers are smaller than among married mothers
despite the fact that lone mothers suffer greater socioeconomic
disadvantage and ill health than married mothers. But one should expect
to find greater relative social class differences in adverse perinatal
outcome rates among married mothers simply because adverse outcome rates
are lower among married mothers. The relative difference in avoiding
these outcomes, however, generally would be smaller.[2,4,6]
For the same reason, inequalities measured in relative rates of
experiencing adverse perinatal outcomes will tend to be greater among the
(lower risk) age 20-34 group than the (higher risk) under 20 group. That
the relative difference is greater among the 35 or above group than the 20
-34 group, assuming that the 35 or above group is at higher risk of
adverse outcomes than the 20-34 group, does not mean the statistical
tendency is not present. Rather, it merely suggests that certain factors,
possibly including the implications of smoking noted by the authors,
outweigh the statistical tendency.
The above observations apply as well to differences in the relative
index of inequality, which measure is largely a function of relative
differences in rates of experiencing an outcome.[2,6] The odds ratios
shown in the tables raise somewhat different issues. In the case of the
outcome frequencies at issue here, odds ratios tend to approximate
relative risks of experiencing the outcome, and the observations regarding
the sizes of such relative risks are unlikely to be less pertinent because
the figures shown in the tables are odds ratio. It is true that relative
difference patterns measured in odds ratios will not vary depending on
whether one examines the adverse or the favorable outcome. But that does
not mean that differences between odds ratios are in some manner
reflecting differences between the sizes of inequalities that are not
solely the function of differences in prevalence in the various settings,
whether defined temporally or demographically, that are being compared.
Like other measures of differences between the rates at which two groups
experience or avoid such outcomes, odds ratios tend to change solely as a
result of changes in prevalence, though less predictably than changes in
relative risks.[2,6]
James P. Scanlan
References
1. Fairley L, Leyland AH. Social class inequalities in perinatal
outcomes: Scotland 1980-2000. J Epidemiol Community Health 2006;601:31-
36.
2. Scanlan JP. Can we actually measure health disparities? Chance
2006:19(2):47-51:
http://www.jpscanlan.com/images/Can_We_Actually_Measure_Health_Disparities.pdf.
3. Scanlan JP. Measuring health disparities. J Public Health Manag
Pract 2006;12(3):294 [Lttr]:
http://www.nursingcenter.com/library/JournalArticle.asp?Article_ID=641470.
4. Scanlan JP. Race and Mortality. Society. 2000;37(2):19-35:
http://www.jpscanlan.com/images/Race_and_Mortality.pdf.
6. Scanlan JP. The misinterpretation of health inequalities in the
United Kingdom. Paper presented at: British Society for Population Studies
Annual Conference 2006, Southampton, England, Sept. 18-20, 2006:
http://www.jpscanlan.com/images/BSPS_2006_Complete_Paper.pdf.
The article by Mohindra SK et al brings about clearly the effect of
caste and socioeconomic position on women’s health [1]. If this is the case
in Kerala, which is one of the states with good health indicators in
India, one can imagine what it would be with more poorer and deprived
states in India. We believe that along with socioeconomic status and
caste, female literacy is one of the key determinant...
The article by Mohindra SK et al brings about clearly the effect of
caste and socioeconomic position on women’s health [1]. If this is the case
in Kerala, which is one of the states with good health indicators in
India, one can imagine what it would be with more poorer and deprived
states in India. We believe that along with socioeconomic status and
caste, female literacy is one of the key determinants of women's health in
general and Sexual and Reproductive Health in particular. In order to
demonstrate this and add more to what has been highlighted in the article
we present data from select Indian states.
The table compares the literacy rates, contraceptive use and birth
order of 3 or more, in eight states of India. The states are grouped into
two categories based on the literacy status. One can observe that as the
literacy status improves so would the contraceptive use and as a
consequence of that the birth order would decrease (Table 1). Lack of
education can have detrimental effects to change in demographics as well.
For example, it has been observed in India that the literacy rate among
Hindus is 65.1% while that among Muslims is 59.1% and one can draw a
relationship of this to the fact that growth rate of Hindus have decreased
from 23.7% in 1961-71 to 20.3% in 1991-2001, whereas that for the Muslims
have increased from 30.8% to 36.0% during the same period. [2]
We believe that it is important to empower women with education to
help them make healthier choices about their health.
References
1. Mohindra S K, Haddad S and Narayana D. Women’s health in a rural
community in Kerala, India: do caste and socioeconomic position matter.
Journal of Epidemiology and Community Health. Dec 2006; 60;19:1020-1025
2. Census of India. available at
http://www.censusindia.net/religiondata/statement.pdf (accessed on 05-12-
2006)
3. Census of India. available at
http://www.censusindia.net/t_00_006.html (accessed on
05-12-2006)
4. Reproductive and Child Health. Summary Report-INDIA. 2002-2004.
available at http://www.rchindia.org/sr/chep5.pdf (accessed on 05-12-2006)
Well researched and well written paper, indeed.
We japanese as a whole don't even know what is going to happen.
Only the powerful Ministry of Finance and Japan Tobacco Co know
what is going on. I hope everybody in Japan read this article.
By doing so, their way of people manipulation will slowly change.
Thank you for your in depth research work.
Dear Editor
Miguel Delgado-Rodríguez and Javier Llorca’s article on bias in health services and medical research is instructive and cautionary [1]. The extensive glossary of biases is thought provoking and might beneficially be introduced as required reading for all researchers.
The glossary might be usefully updated by the addition of a form of selection bias which is very much “of our time”, having be...
Dear Editor
In the latest issue of the JECH, Rezeaiean, Dunn, St Leger and Appleby provide a multidisciplinary glossary on geographical epidemiology, spatial analysis and geographical information systems. The glossary in large is useful as it gives an overview of relevant methodological concepts. However, in the section on disease clustering the Authors shortly describe geographical machine analysis and spatial sc...
Editor - In this issue of the journal, Dr Peter John Aspinall has raised a very important issue regarding the question of whether colour categories for ethnic groups should be abandoned because of abolishment of colour categories in the Scotland census.[1]
The Scottish population census team deserves congratulations for breaking the tradition in abandoning the colour categories used in 1991 and 2001.[2] This bol...
Dear Editor
Studies of changing inequalities in receipt of procedures like that carried out with respect to revascularization by Hetemaa et al.[1] need to be undertaken with an appreciation of the statistical tendency whereby the rarer an outcome the greater the relative difference in rates of experiencing it and the smaller the relative difference in rates of avoiding it.[2-6]
Most research into inequa...
Dear Editor
In their prospective study, Baibas et al (JECH 2005;59(4):274- 8)showed that, in a Greek mountain village at 950 metres, total mortality and not merely coronary mortality was lower than in two lowland villages. What follows assumes that the cancer figures included within "other causes" follow this pattern.
In 'Geographic Cancer Risk and Intracellular Potassium/Sodium ratios'.Cancer Detection...
Dear Editor
...
Dear Editor,
I wish to thank Dr Hanna and her colleagues for this excellent study (1). I would like to share below a few comments....
Dear Editor
The results of the study by Fairley and Leyland [1] of changing social class inequalities in perinatal outcomes in Scotland must be interpreted in light of the statistical tendency whereby the rarer an outcome the greater the relative differences in experiencing the outcome and the smaller the relative difference in avoiding the outcome.[2-6] In times of declines in adverse outcome (the more common...
Dear Sir,
The article by Mohindra SK et al brings about clearly the effect of caste and socioeconomic position on women’s health [1]. If this is the case in Kerala, which is one of the states with good health indicators in India, one can imagine what it would be with more poorer and deprived states in India. We believe that along with socioeconomic status and caste, female literacy is one of the key determinant...
Dear Editor
Well researched and well written paper, indeed. We japanese as a whole don't even know what is going to happen. Only the powerful Ministry of Finance and Japan Tobacco Co know what is going on. I hope everybody in Japan read this article. By doing so, their way of people manipulation will slowly change. Thank you for your in depth research work.
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